Parents (Total: 24)

The searched file hash is included in 24 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	C9F904FA1BDF623CAE8396C0749DE5FD
PackageArch	x86_64
PackageDescription	Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family. This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers listed in the URL below.
PackageName	R-glmnet
PackageRelease	lp153.1.1
PackageVersion	4.1.3
SHA-1	27B93867AE1ECCB4D417F4517154114F35B9A824
SHA-256	6AD689DB0BE8A7A847945EDCE2B37472E1DECD397D3DB950670AEB354F56D2FB

Key	Value
FileSize	1784572
MD5	865BE0E276B721CCAB6ECAE06D652A39
PackageDescription	Lasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	4.1-2
SHA-1	2B9D5C7977E69C0375FBEBECC1A9D377E1048D47
SHA-256	827BE2B261B783CDF483E6B488512B1D3A9EDB35B73D592624E5C9EC94FF3547

Key	Value
FileSize	1782936
MD5	A95EC865448CF36EA5F2E44CCD3A6E70
PackageDescription	Lasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	4.1-2
SHA-1	2D8FF03F8FD5D29923A1236B670B33AFD7C33DD5
SHA-256	96D94CC49D85CC4D8667B8920C0F071F60F8DAE9840A76F964D4F3B5A851E6B2

Key	Value
FileSize	1771080
MD5	C660FC0A571F7EB1DCEE6E59A0DA8E84
PackageDescription	Lasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	4.1-2
SHA-1	3789845F0DFF284065234D1A236D9697FC997A8E
SHA-256	871934825BA7C6033904FA8633E793B46AE5BD564A2922708DC1117A5854DAE1

Key	Value
FileSize	1790636
MD5	0FDDF512F1FA49E1C58B6B0642246758
PackageDescription	Lasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	4.1-2
SHA-1	503CCC991AA0BDEAB2E66CCD239093B948761A71
SHA-256	32FB520FDDE1FE9B438CDD7539605714C13B84776763A3DCB2C3E48F64E85BD3

Key	Value
FileSize	1820660
MD5	D925D09EE1BD36FD292ADE96FE596D20
PackageDescription	Lasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	4.1-2-1
SHA-1	51EFA388BA795A1AB6ED154216A4606490486AA4
SHA-256	33D35E6255A7E916CEDA20D3C6669931120645A766B5C612DB0FFC503CB954D5

Key	Value
MD5	273703A00DF54B82A21C4D6D80979CC5
PackageArch	x86_64
PackageDescription	Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family. This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers listed in the URL below.
PackageName	R-glmnet
PackageRelease	1.5
PackageVersion	4.1.3
SHA-1	61C617CA894F98F158A30F108955341F359D5E4A
SHA-256	C588AEE5F3B23D51D47D1B9C9AC45DB8F69A3BD989B8045A504852B3A9285B1C

Key	Value
FileSize	1809332
MD5	07793E4A27D9ED2FF09BEFD737C9D207
PackageDescription	Lasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	4.1-2-1
SHA-1	6B706090536E321533ED8FF3957B4C575E0B4347
SHA-256	99C4E5F57E03A1C8736D254998F9FC53E287849526D0FE625B24B69EE93BA2AC

Key	Value
FileSize	1777680
MD5	D2D31253673425EF5760936999E9622E
PackageDescription	Lasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	4.1-2
SHA-1	6E6D70092464B3D26A65561E29824ECEBF79DDF4
SHA-256	93204997EC5CCA41B1CA2485DF218F0961D61B561315B634965EE551EDB32F1C

Key	Value
MD5	92B92894E5880A87FA6A1A4683E6EFC5
PackageArch	x86_64
PackageDescription	Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family. This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers listed in the URL below.
PackageName	R-glmnet
PackageRelease	lp152.1.1
PackageVersion	4.1.3
SHA-1	957C313036C1A6868DBD28F221D88CC133233C79
SHA-256	A99F3D285491117F397E34F572BE4F442904A8D704BF057FB66AF076B7AC1B37